Continuous quality monitoring in DevOps is the process of identifying threats to the security and compliance rules of a software development cycle and architecture. Also known as continuous control monitoring or CCM, this is an automated procedure that can be extended to detect similar inconsistencies in IT infrastructures. Continuous monitoring helps business and technical teams determine and interpret analytics to solve crucial issues, as mentioned above, instantaneously. Digital experience monitoring, or DEM, on the other hand, is the process of optimizing the operational behavior and experience of a system.
Continuous monitoring or CM is a step towards the end of the DevOps process. The software is usually sent for production before continuous monitoring is conducted. CM informs all relevant teams about the errors encountered during the production period. Once detected, these flaws are then looked into by the people concerned. DevOps tools for continuous monitoring include Prometheus, Monit, Datadog, and Nagios.
Continuous control monitoring goes a long way to help enterprises acquire data from various ecosystems, which can then be used to take more robust security measures like threat assessment, quick response to breaches, root cause analysis, and cyber forensics. Moreover, continuous quality monitoring keeps a tab and reports on the overall well-being of the DevOps setup.
Aim of Continuous Monitoring in DevOps
Continuous monitoring in DevOps has a few specific goals, as are mentioned below:
Many components of software operations can trigger devastating outcomes like breaches. Continuous monitoring aims to strengthen the transparency of such environments while keeping in place a vigilant system to monitor and resolve said issues.
Continuous monitoring aims to identify performance inconsistencies and error sources. It also resolves these problems using relevant solutions to safeguard the enterprise.
Continuous monitoring assists companies in keeping a tab of their user experience. CM is especially helpful in tracking user feedback after a recent change or update to a software or an application. This helps firms build and strengthen their business strategies.
Creating and maintaining IT infrastructure in-house is imperative for companies that depend heavily on this system to develop and deliver services and products. IT infrastructures typically include components like storage, software and hardware units, data centers, servers, networks, and so on. Infrastructure monitoring supervises this environment to assist businesses in making their products better and more sustainable.
Infrastructure monitoring tools must take care of the following tasks:
A server's availability determines its ability to handle its tasks efficiently. Infrastructure monitoring tools should observe this attribute of a server.
Another metric that determines the efficiency of an IT ecosystem is the usage of disks and the CPU. Inefficient systems can take more time to execute a given task, thereby hampering the performance of the setup. Infrastructure monitoring tools can avoid this by tracking CPU and disk usage and providing feedback if issues arise.
Infrastructure monitoring tools must show how reliable a system is by tracking and monitoring its runtime.
Errors are inevitable parts of machinery. They can be reduced but never eliminated. An infrastructure monitoring tool is responsible for tracking the response time of a system if and when an error occurs.
Besides the above, these tools must also monitor security, storage, permissions, the well-being of databases, network switches, performance, and so on.
An open-source tool that helps with infrastructure monitoring.
Nagios makes probing log data a much simpler procedure.
Monitors Java Management Extensions, server OS, and desktops.
Prometheus
An open-source infrastructure monitoring tool that accumulates and analyses data related to time series.
Prometheus uses Simple Network Management Protocol, popularly called SNMP, to acquire performance figures from across the network.
Accumulates and preserves data.
Significantly reduces the time it takes to set up an environment.
Works well with ecosystems like Kubernetes and Docker.
Prometheus can integrate and work well with Salt, Puppet, and so on.
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Application Monitoring
Application monitoring provides intel about an application- everything from application uptime, security to performance and log-time. Application monitoring tools typically track API responses, transaction time and volume, system responses, and the well-being of the application ecosystem. The reports are generated mainly in the form of statistics and graphs that are immensely helpful in understanding how well the application is performing.
Application monitoring tools must take care of the following tasks:
SLA or service-level agreement is a contract between a vendor and their buyer that outlines the clauses of the services that the former agrees to provide. Application monitoring tools should be agile in tracking the SLA status.
These tools must be adept at computing and analyzing the frequency of errors in an application.
Application monitoring tools must be vigilant about transactions at the user's end, pages that take time to load, browser speed, and the speed of resources provided by external operators.
This is a paid tool that aids in monitoring applications, services, databases, servers using a SaaS environment.
Datadog supports more than 80 integrations for customized systems.
Datadog provides insight into the performance of all tools required in the DevOps cycle.
The tool also helps in the visualization of data related to upstream and downstream environments.
Splunk
A paid tool that explores examines and evaluates machine data accumulated from the IT ecosystem.
Splunk provides operational intelligence, data analytics, and a coalesced vision of all IT services offered to backend teams.
Splunk supports customized security solutions that can be added to the platform as extensions.
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Network Monitoring
Network monitoring handles one of the most crucial components of the DevOps process- networks. The tools facilitate the evaluation of switches, servers, virtual machines, firewalls, and routers. Network monitoring encompasses five tasks- discovering, delineating, detecting, observing, and reporting. Such systems are proactive in finding flaws and optimizing the performance and availability of the components they monitor.
Network monitoring tools should perform the following tasks:
These should monitor latency and the bandwidth of the server.
Network monitoring tools should also be adept at tracking the flow of packets in a network.
DevOps Tools for Networking Monitoring
Wireshark
One of the best tools to monitor and analyze the traffic in a network.
Tracks dubious activities, lost packets, and latency.
Free tool but usually requires excellent expertise to use.
Captures and translates packets into a binary format for human readability.
NMap- Network Mapper
An open-source network monitoring tool that aids in identifying vulnerabilities.
NMap monitors singular hosts and massive networks consisting of a large number of subnets.
NMap allows its users to collect and identify features of the web with the use of collected data.
Continuous monitoring is a critical component of the DevOps lifecycle, enabling teams to detect and address issues in real time, improve security, and ensure the reliability of applications. Several tools have emerged as leaders in the field, each offering unique features tailored to different aspects of continuous monitoring:
Prometheus: An open-source monitoring solution that is particularly well-suited for time-series data. It's widely used for its powerful querying language and alerting capabilities, making it ideal for tracking application performance metrics.
Grafana: Often used with Prometheus, Grafana is a visualization tool that lets teams create metrics dashboards. It supports multiple data sources, providing a versatile platform for monitoring insights.
Nagios: A veteran in the monitoring space, Nagios offers comprehensive monitoring capabilities, including network monitoring, server, and application monitoring. It's known for its robust alerting system and extensive plugin ecosystem, allowing for high customization.
ELK Stack (Elasticsearch, Logstash, Kibana): This stack is pivotal for log monitoring and analysis. Elasticsearch helps with search and analytics, Logstash is used for log ingestion and processing, and Kibana provides visualization capabilities. Together, they form a powerful toolset for understanding and acting on log data.
Datadog: A cloud-based service that provides monitoring of servers, databases, and services through a SaaS-based data analytics platform. Datadog is known for its ability to provide end-to-end visibility across dynamic, high-scale infrastructure.
Selecting the right tool(s) depends on specific project requirements, infrastructure complexity, and the specific metrics or logs that need monitoring. Integration capabilities with existing DevOps tools and workflows should also be considered to ensure seamless operation.
Best Practices for Continuous Monitoring in DevOps
Implementing continuous monitoring effectively requires more than just the right tools; it demands a strategic approach. Here are some best practices to guide your continuous monitoring strategy in DevOps:
Define Clear Monitoring Objectives: Identify what you need to monitor and why. Setting clear objectives helps select the right tools and metrics to focus on.
Automate Wherever Possible: Automation is a core principle of DevOps. Apply it to your monitoring processes to reduce manual effort, speed up response times, and increase reliability.
Integrate Monitoring Throughout the DevOps Lifecycle: Continuous monitoring should be integrated into each stage of the DevOps pipeline, from development to deployment, to ensure comprehensive visibility.
Leverage Real-time Alerts and Dashboards: Real-time alerts help promptly identify and address issues, while dashboards provide a consolidated view of the health of your systems and applications.
Incorporate Feedback Loops: Use insights gained from monitoring to inform development and operational decisions. Continuous feedback loops help refine processes, improve application performance, and enhance user satisfaction.
Ensure Scalability and Flexibility: Choose tools and practices that can scale with your infrastructure and adapt to changing needs. This is crucial for maintaining effectiveness as your system evolves.
Prioritize Security and Compliance: Monitor security metrics and compliance postures continuously to protect against vulnerabilities and ensure adherence to regulatory standards.
With these best practices, organizations can create a robust continuous monitoring framework that enhances their applications' reliability, performance, and security, contributing to the overall success of their DevOps initiatives.
Benefits of Continuous Monitoring
Better security: Continuous quality monitoring can be utilized to automate many security measures. CM analyzes data across the entire ecosystem, giving backend teams a broad spectrum of visibility throughout the environment. This helps identify inconsistencies and trigger events that can lead to security failures. System admins can identify security threats and respond to them much faster due to continuous monitoring. Quicker feedback and real-time reports help security teams avert breach attempts and lessen the aftermath should an attack occur.
Performance errors are detected earlier: Continuous monitoring is flexible in its entry into the software development cycle. Although CM is traditionally introduced during the production phase, initiating it in staging and testing environments can help determine performance inconsistencies much ahead of time. The production cycle can thus deal only with more stable releases.
System downtime is significantly reduced: System downtimes are infamous for causing significant disruptions in business operations, where recurring incidents can lead to a loss in revenue. Continuous quality monitoring can assist technical teams to keep a vigilant eye on the database, network, and applications, helping to resolve any issues before they give rise to system downtime. Past issues are also evaluated to avoid them in the future and to build more enhanced software solutions.
Facilitates better performance for the business: Since continuous monitoring works to alleviate system downtimes, it improves user experience and business credibility. Moreover, continuous monitoring is used extensively to track user feedback that comes in handy when evaluating new updates and changes to the system.
Continuous monitoring is a crucial automation procedure in the software development lifecycle. It aims to enhance IT ecosystems' transparency, performance, and user experience. There are three types of continuous quality monitoring in place -
Infrastructure monitoring that supervises the hardware and software units, storage, servers, and so on.
Application monitoring that tracks API and system responses, transaction time and volume, and the health of the application overall.
Network monitoring that discovers, delineates, detects, observes, and reports inconsistencies in a network.
Continuous monitoring thus improves security, detects and eliminates performance errors, reduces system downtimes, and aids in building better business strategies to drive growth and revenue.
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Ans: Continuous monitoring is used to assess the risk and compliance problems within an enterprise's environment, especially its IT infrastructure.
Q2: What are the essential components of continuous monitoring?
Ans: The following are the most critical components of continuous monitoring:
Network inventories
Ports, network ecosystems, protocols, and the services they support
Features of the system
Scanning, identifying, and resolving inconsistencies and vulnerabilities in the system.
Q3: What tools can be used for continuous monitoring?
Ans: Tools like Nagios, Monit, Consul, and PagerDuty can be used for continuous monitoring.
Q4: What is continuous delivery?
Ans: Improvements are continuously applied to the software throughout the development and testing process in continuous delivery. This is facilitated with the help of automated tools. HeadSpin, the world’s first digital experience AI platform, provides continuous quality monitoring and digital experience monitoring (DEM) services. We help our clients achieve their business goals with the help of data-driven insights and KPIs most relevant to their business.
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